[t5] Fix T5 inference in float16 + bnb error (#21281)

* attempts to fix:

- upcast input for `T5DenseActDense`
- add the condition `self.wo.weight.dtype != torch.int8`
- added tests on `test/mixed_int8`
- `make fixup`

* fix ci test

src/transformers/models/longt5/modeling_longt5.py

@@ -276,6 +276,8 @@ class LongT5DenseActDense(nn.Module):
         hidden_states = self.wi(hidden_states)
         hidden_states = self.act(hidden_states)
         hidden_states = self.dropout(hidden_states)
+        if hidden_states.dtype != self.wo.weight.dtype and self.wo.weight.dtype != torch.int8:
+            hidden_states = hidden_states.to(self.wo.weight.dtype)
         hidden_states = self.wo(hidden_states)
         return hidden_states
 

src/transformers/models/mt5/modeling_mt5.py

@@ -146,6 +146,8 @@ class MT5DenseActDense(nn.Module):
         hidden_states = self.wi(hidden_states)
         hidden_states = self.act(hidden_states)
         hidden_states = self.dropout(hidden_states)
+        if hidden_states.dtype != self.wo.weight.dtype and self.wo.weight.dtype != torch.int8:
+            hidden_states = hidden_states.to(self.wo.weight.dtype)
         hidden_states = self.wo(hidden_states)
         return hidden_states
 
@@ -168,7 +170,8 @@ class MT5DenseGatedActDense(nn.Module):
 
         # To make 8bit quantization work for google/flan-t5-xxl, self.wo is kept in float32.
         # See https://github.com/huggingface/transformers/issues/20287
-        if hidden_states.dtype != self.wo.weight.dtype:
+        # we also make sure the weights are not in `int8` in case users will force `_keep_in_fp32_modules` to be `None``
+        if hidden_states.dtype != self.wo.weight.dtype and self.wo.weight.dtype != torch.int8:
             hidden_states = hidden_states.to(self.wo.weight.dtype)
 
         hidden_states = self.wo(hidden_states)

src/transformers/models/switch_transformers/modeling_switch_transformers.py

@@ -273,6 +273,8 @@ class SwitchTransformersDenseActDense(nn.Module):
         hidden_states = self.wi(hidden_states)
         hidden_states = self.act(hidden_states)
         hidden_states = self.dropout(hidden_states)
+        if hidden_states.dtype != self.wo.weight.dtype and self.wo.weight.dtype != torch.int8:
+            hidden_states = hidden_states.to(self.wo.weight.dtype)
         hidden_states = self.wo(hidden_states)
         return hidden_states
 

src/transformers/models/t5/modeling_t5.py

@@ -289,6 +289,8 @@ class T5DenseActDense(nn.Module):
         hidden_states = self.wi(hidden_states)
         hidden_states = self.act(hidden_states)
         hidden_states = self.dropout(hidden_states)
+        if hidden_states.dtype != self.wo.weight.dtype and self.wo.weight.dtype != torch.int8:
+            hidden_states = hidden_states.to(self.wo.weight.dtype)
         hidden_states = self.wo(hidden_states)
         return hidden_states
 
@@ -310,7 +312,8 @@ class T5DenseGatedActDense(nn.Module):
 
         # To make 8bit quantization work for google/flan-t5-xxl, self.wo is kept in float32.
         # See https://github.com/huggingface/transformers/issues/20287
-        if hidden_states.dtype != self.wo.weight.dtype:
+        # we also make sure the weights are not in `int8` in case users will force `_keep_in_fp32_modules` to be `None``
+        if hidden_states.dtype != self.wo.weight.dtype and self.wo.weight.dtype != torch.int8:
             hidden_states = hidden_states.to(self.wo.weight.dtype)
 
         hidden_states = self.wo(hidden_states)

tests/mixed_int8/test_mixed_int8.py

@@ -163,6 +163,70 @@ class MixedInt8Test(BaseMixedInt8Test):
         self.assertTrue(model.decoder.block[0].layer[2].DenseReluDense.wo.weight.dtype == torch.float32)
 
 
+@require_bitsandbytes
+@require_accelerate
+@require_torch
+@require_torch_gpu
+@slow
+class MixedInt8T5Test(unittest.TestCase):
+    @classmethod
+    def setUpClass(cls):
+        cls.model_name = "t5-small"
+        cls.dense_act_model_name = "google/flan-t5-small"  # flan-t5 uses dense-act instead of dense-relu-dense
+        cls.tokenizer = AutoTokenizer.from_pretrained(cls.model_name)
+        cls.input_text = "Translate in German: Hello, my dog is cute"
+
+    def tearDown(self):
+        r"""
+        TearDown function needs to be called at the end of each test to free the GPU memory and cache, also to
+        avoid unexpected behaviors. Please see: https://discuss.pytorch.org/t/how-can-we-release-gpu-memory-cache/14530/27
+        """
+        gc.collect()
+        torch.cuda.empty_cache()
+
+    def test_inference_without_keep_in_fp32(self):
+        r"""
+        Test whether it is possible to mix both `int8` and `fp32` weights when using `keep_in_fp32_modules` correctly.
+        `flan-t5-small` uses `T5DenseGatedActDense` whereas `t5-small` uses `T5DenseReluDense`. We need to test
+        both cases.
+        """
+        from transformers import T5ForConditionalGeneration
+
+        T5ForConditionalGeneration._keep_in_fp32_modules = None
+
+        # test with `t5-small`
+        model = T5ForConditionalGeneration.from_pretrained(self.model_name, load_in_8bit=True, device_map="auto")
+        encoded_input = self.tokenizer(self.input_text, return_tensors="pt").to(0)
+        _ = model.generate(**encoded_input)
+
+        # test with `flan-t5-small`
+        model = T5ForConditionalGeneration.from_pretrained(
+            self.dense_act_model_name, load_in_8bit=True, device_map="auto"
+        )
+        encoded_input = self.tokenizer(self.input_text, return_tensors="pt").to(0)
+        _ = model.generate(**encoded_input)
+
+    def test_inference_with_keep_in_fp32(self):
+        r"""
+        Test whether it is possible to mix both `int8` and `fp32` weights when using `keep_in_fp32_modules` correctly.
+        `flan-t5-small` uses `T5DenseGatedActDense` whereas `t5-small` uses `T5DenseReluDense`. We need to test
+        both cases.
+        """
+        from transformers import T5ForConditionalGeneration
+
+        # test with `t5-small`
+        model = T5ForConditionalGeneration.from_pretrained(self.model_name, load_in_8bit=True, device_map="auto")
+        encoded_input = self.tokenizer(self.input_text, return_tensors="pt").to(0)
+        _ = model.generate(**encoded_input)
+
+        # test with `flan-t5-small`
+        model = T5ForConditionalGeneration.from_pretrained(
+            self.dense_act_model_name, load_in_8bit=True, device_map="auto"
+        )
+        encoded_input = self.tokenizer(self.input_text, return_tensors="pt").to(0)
+        _ = model.generate(**encoded_input)
+
+
 class MixedInt8ModelClassesTest(BaseMixedInt8Test):
     def setUp(self):
         super().setUp()